Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors.

Journal: ACS nano
Published Date: Sep 20, 2021

Abstract

The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information ( visual, pressure, sound, ) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.

Authors

  • Yifei Pei
    National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
  • Lei Yan
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Zuheng Wu
    Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, 3 Beitucheng West Road, Beijing, 100029, China.
  • Jikai Lu
    Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  • Jianhui Zhao
    National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
  • Jingsheng Chen
    Department of Materials Science & Engineering, National University of Singapore, Singapore, 117575, Singapore.
  • Qi Liu
    National Institute of Traditional Chinese Medicine Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China.
  • Xiaobing Yan
    Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Key Laboratory of Optoelectronic Information Materials of Hebei Province, Hebei University, Baoding, Hebei, 071002, China.

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